Data is the fuel for building better products and with easy access to data a virtuous cycle of more data, better products, and more users gets created.
Data continues to grow at an exponential rate as information continues to come from multiple digital platforms. However, most of these data stay unused, and that’s why we need data-driven Product Managers who can ask the right questions and can work along with data analysts and scientists. Data PMs are at the forefront when it comes to developing the next generation of products and solutions which can capture the value generated from data and has the potential to impact the lives of many people. These product managers not only work with the traditional teams of software engineers, designers, and marketers but also collaborate with data scientists, data engineers, and machine learning researchers.
“Working with data at the core of a product requires a level of understanding of data modeling, data infrastructure, statistical and machine learning. If the traditional PM operates at the intersection of business, engineering, and user experience, the data PM must also have domain knowledge of data and data science.” — Trey Causey, Rise of the Data Product Manager
What does it take to become a data powered Product Manager?
Yes, data is all about numbers and charts, while some familiarity in reading numbers is essential one need not be an expert mathematician or a master statistician. Either you can choose to learn the basics of high school mathematics and statistics and start playing around yourself with the data or decide to work with people in the organization who are data experts say data analysts or scientists. Product Managers must live and breathe data for making product-related decisions. Below is my take for someone who wants to hone the skills and become a data-driven Product Manager.
Develop Business Knowledge: Understanding the domain and the business strategy behind the product and its features is vital for any PM. Every product has some key features, and one shall look into data about that product. Data is growing at an exponential rate, and it will be turn out to be a futile exercise if someone tries to explore anything and everything out there. When I joined PayPal from a Corporate Banking experience, it took me a while to familiarize myself with the plethora of data, and I am thankful to my colleagues who helped me navigate and explore the data. While I am still learning and exploring different data sets available at my disposal, it is crucial to link the data back to the product. Action Step: Spend time with architects and engineers to help you understand the high-level data mapping and identify critical areas for your application before you start analyzing the data.
Analyze the Data: Assuming that you have built the basic product understanding and have an understanding of what kind of data is available, now is the time to analyze the data. Analysis of data depends solely on how complex the data set is and what exactly we are looking for, preparing a list of questions and hypothesis shall be the first step before we dig ourselves deep into the data. You might go about collecting the data yourself or ask support from your analytics team; however, having the right set of questions for the data to be collected must come from a PM. Now that you have your hypothesis ready, and have managed to collect the data, spend time validating your hypothesis. A basic understanding of excel shall be good enough for you to analyze the data. For most scenarios, you need not run a one-tailed or two-tailed hypothesis test, and your deductions shall be solely on high-level data points and your intuition. Action Step: Prepare a list of questions and hypotheses before requesting the data, validate the data using excel, and be ready to train yourself if need be.
Train Yourself: Most of the companies today have dedicated data analytics teams consisting of data analysts, data scientists, and data visualization experts, and they will be the eyes and ears for everything data. However, it is practically impossible for the lean data team to cater to the needs of multiple PMs working on independent products and hence the need to train yourself as a PM. I am not saying that you must become a data analyst and learn all the tools and techniques of the trade. Still, the know-how of database table structure, columns mapping, simple SQL queries, and database tools is good enough to get started and free up the dependency on the data analytics team. Learning these skills will enable you as a PM to do the groundwork before requesting complex data from the data analytics team. One will be better prepared to answer questions related to the data and create a hypothesis. Action Step: Dedicate focus time regularly to understand the table structures and practice SQL queries, seek help from engineers and data analysts to build data acquaintance.
Act-on the Data: Generating insights and structuring the data in a presentable format is the final step. There are various data visualization tools that you can use to generate presentations that are easy to digest by the executives and key decision-makers. Data visualization shall create a pictorial representation of the insights generated from the data and make it easier to enable actions that will help the business. An easily understandable format with a 360-degree view will create the data more meaningful and would allow executives to get an accurate picture. Action Step: Understand how data visualization plays a pivotal role in generating insights from the data and factor in time to ensure that data presentation will enable the action.
There are several free online resources for us to work on each of the action steps, depending on where we are in the journey. I am happy to share a few of the links to help you get started in becoming a data powered Product Manager.
- Payments Knowledge: https://www.pymnts.com/payology/course/payology/
- Data Analysis using Excel:
https://www.excel-easy.com/data-analysis.html
https://excelwithbusiness.com/blog/15-excel-data-analysis-functions-you-need-to-know/ - Learning SQL Basics:
https://www.w3schools.com/sql/ - Data Visualization using Tableau:
https://www.tableau.com/learn/training
Happy Learning !!
